code: def eval(self, data):
      if isinstance(data, dict):
          data_chunked = [{} for _ in self.nodes]
          for k, v in data.items():
              v_chunks = np.array_split(v, len(self.nodes), axis=0)
              for idx, v_chunk in enumerate(v_chunks):
                  data_chunked[idx][k] = v_chunk
  
          res = []
          for n, d in zip(self.nodes, data_chunked):
              res.append(n.eval.remote(d))
  
          total = 0
          correct = 0
          last_correct = 0
  
          total_last_loss = 0
          mask_loss = []
          each_correct = []
  
          for input, output in zip(data_chunked, ray.get(res)):
              correct_and_valid = np.logical_and(
                  output["correct"], input["eval_mask"]
              )
  
              correct_tokens_count = np.sum(correct_and_valid, -1)
              valid_tokens_count = np.sum(input["eval_mask"], -1)
  
              correct_example = np.logical_and(
                  valid_tokens_count == correct_tokens_count,
                  valid_tokens_count > 0,
              )
              valid_example = valid_tokens_count > 0
              last_correct_example = correct_and_valid[:, -1]
  
              each_correct += correct_example.tolist()
  
              total += sum(valid_example)
              correct += sum(correct_example)
              last_correct += sum(last_correct_example)
              total_last_loss += sum(
                  valid_example * output["last_loss"]
              )
  
              valid_loss = np.sum(
                  output["all_loss"] * input["eval_mask"], -1
              )
              mask_loss += valid_loss.tolist()
  
          return {
              "total": total,
              "correct": correct,
              "last_correct": last_correct,
              "last_loss": total_last_loss,
              "mask_loss": np.array(mask_loss),
              "each_correct": np.array(each_correct),
          }
      else:
          data_chunks = np.array_split(data, len(self.nodes), axis=0)
  
          res = []
          for n, d in zip(self.nodes, data_chunks):
              res.append(
                  n.eval.remote(
                      {
                          "obs": d[:, :-1],
                          "target": d[:, 1:],
                      }
                  )
              )
  
          return np.array([i["loss"] for i in ray.get(res)]).mean()
  
file_path: mesh-transformer-jax/mesh_transformer/TPU_cluster.py
imports: import itertools
  import json
  import time
  import ray
  from typing import Callable
  import numpy
  from mesh_transformer.train_actor import NetworkRunner
  from google.cloud import storage
  from smart_open import open
  from func_timeout import func_set_timeout
label_desc: Use image node_id 3 for calling the TPUCluster obj's underlying member method code with example usage: obj.eval(data) and returns: dict, np